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SUMMARY: In this work, an adaptive observer‐based control scheme has been designed for uncertain nonaffine nonstrict feedback systems subject to state time delay, various types of input nonlinearities, time‐varying asymmetric state constraints, and unknown control direction. Compared to the existing controllers for systems with state constraints, the designed control scheme in this work can be applied to state‐constrained systems with nonaffine structure subject to state delays, unknown control direction, and different types of input nonlinearities, while full‐states measurement is not required. Moreover, by introducing a novel saturated Nussbaum function in the present work, not only has the problem of unknown control direction been handled, but also the probability of a control shock due to applying the conventional Nussbaum functions has been eliminated. The system unknown dynamics has been approximated by using neural networks (NNs), and by updating the maximum norm of NNs weight vectors, only one adaptive law is required for the controller implementation. By employing appropriate Lyapunov functions, it has been shown that all signals in the closed‐loop system are bounded, state constraints are satisfied, and the output tracking error can be made small by selecting appropriate design parameters. The effectiveness of the designed control scheme has been shown through two simulation studies. Moreover, the superiority of the proposed saturated Nussbaum function in comparison with conventional Nussbaum functions has been shown via simulation. [ABSTRACT FROM AUTHOR]

A novel reinforcement learning-based adaptive optimal controller is proposed to obtain the desired tracking performance for a class of nonlinear nonstrict feedback systems with uncertain dynamics in this paper. The main feature is that the proposed control scheme can handle the control problem that traditional reinforcement learning-based algorithm cannot deal with. To achieve the optimal control of the high-order system, the virtual and the actual control of the system are optimized by using reinforcement learning method. Radial basis function neural networks are employed to approximate the uncertain system dynamics, the optimal cost function and the optimal control law, respectively. According to Lyapunov stability theorem, it is proved that all the error signals in the closed-loop systems are semi-globally uniformly ultimately bounded (SGUUB) while the desired tracking control performance can be obtained. Simulation results are given to illustrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy decentralized control algorithm is developed in this article for interconnected nonlinear large-scale systems with unmodeled dynamics. This method is proposed by applying a fuzzy logic system (FLS) to identify unknown nonlinear functions. In addition, dynamic signals are introduced in design process of the backstepping to compensate the effect of unmodeled dynamics. A novel adaptive state feedback control algorithm is proposed with the help of Lyapunov function. Then, the state feedback control algorithm is extended to the case of output feedback by constructing a fuzzy state observer with FLSs. The large-scale and closed-loop nonlinear system is ensured to be semi-global uniformly ultimately bounded (SGUUB). Apart from this, all signals are guaranteed to be bounded. Both numerical and practical simulation examples are utilized to elaborate the feasibility of the developed control algorithms. [ABSTRACT FROM AUTHOR]

This article proposes an adaptive dynamic surface fuzzy control method for a class of nonstrict feedback systems. The systems are subject to state constraints with unknown control directions, time-delay, and external disturbances. The problem of unknown control direction is solved by using the Nussbaum gain technique, and the effect of unknown delay which is time varying is eliminated by introducing compounding type Lyapunov function. Furthermore, based on the fuzzy backstepping method, an adaptive tracking controller is constructed which guarantees that the constrained states are not violated while all the closed-loop trajectory signals remain bounded. The effectiveness of the obtained results is illustrated via simulations. [ABSTRACT FROM AUTHOR]

In this paper, an observer-based fuzzy adaptive controller for a class of uncertain nonstrict nonlinear systems with unknown control direction and unknown dead-zone is presented. First, by using equivalence dead-zone inverse and a linear state transformation, the original system is converted to a new one. Then, by using fuzzy logic systems, the unknown nonlinearities are approximated based on an adaptive mechanism, and a nonlinear fuzzy state observer is designed to estimate immeasurable states. The dynamic surface control technique is employed to solve the problem of explosion of complexity in the traditional backstepping approach, and then, this method is combined with Nussbaum gain function to address the problem of unknown control direction. Besides, barrier Lyapunov function is employed to overcome the violation of system output. The proposed controller guarantees that the closed-loop system is stable; all the system states are bounded, and tracking errors converge to a neighborhood of the origin. A numerical simulation is provided to confirm the usefulness of the proposed control design. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates an observer-based neuro-adaptive prescribed performance control scheme for nonstrict feedback systems with consideration of unmeasurable states and expected output tracking performance constraint. Firstly, a novel simplified barrier Lyapunov function involving the prescribed performance function is proposed. Then, via combing the backstepping technique and neural network based approximation, an adaptive prescribed performance controller is developed along with a neural network based observer. To conquer the problem "explosion of complexity" inherent in backstepping technique, dynamic surface control technique is employed, wherein, an adaptive nonlinear filter is devised to compensate for the boundary layer errors. Compared with the existing works, the logarithmic function-based transformation for the prescribed performance constraint is avoided, which renders the developed scheme computationally simple. Finally, the application to a one-link manipulator is organized to verify the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]